How to Use the Advantages of Machine Learning in Your Business?

In simple terms, the point is to create an algorithm that is able to teach itself without any external help. Such solutions intend to process input data with the help of mathematical models and a set of parameters. Thus, after the necessary data is obtained - the algorithms in machine learning compares them and improves it's actions all the time.

The technology's main purpose is to develop the algorithms for machine learning with the help of which you can achieve automatic data processing. So, instead of writing additional code, developers may put the data inside of an algorithm that is working on it's self-perfection all the time by searching for data patterns.

However, the system should have access to numerous examples of incoming and outgoing data. The information itself depends on what kind of product you are going to work on.

Applications of machine learning differ. For example, talking about an educational app, to predict pupils' future performance a lot of data should be analyzed. Including their performance from previous years, the time they need to prepare for a test, the time they need to learn something etc.

There are several machine learning techniques:

All these methods are usually applied within media content analysis and Big Data machine learning analysis.

As it was already mentioned, machine learning is a technology that contributes to the development and improvement of dozens of features we have gotten used to. Let's consider the ones we meet most often when using our favorite applications day-to-day and finally learn the advantages of machine learning techniques.

This feature is pretty widespread among mobile applications. For example, it is sometimes used for identification purposes or work with photos including filters and editing. Besides, using different types of machine learning algorithms you can define users' sex and age within an app, implement the recognition of an eye's retina or fingerprint etc. A good example of a machine learning application is the recognition of license plates on roads in case of violations. 

There is no need to look far, Apple's Siri and Google Now use a list of machine learning algorithms to recognize the user's commands and react to them. 

For users, this feature recognizes documents, credit cards, translate foreign words on pictures etc. It is important to take into account that a text has a variety of characteristics such as the font, size and more. That is why every model of algorithms for machine learning should be built having these characteristics in mind.

I am talking about the creation of personalized content that will be able to take the users' preferences into account.

One of the machine learning examples is Netflix that uses algorithms to make it possible for users to see recommendations based on their previous views. Thus, having viewed The Matrix, the service will assume that you probably would also like Equilibrium etc. The ways of making suggestions differ from service to service. Some of them base their suggestions on actors, some on genre etc.

Using personalization, you do not only gain more loyal users but make them spend even more money on your service as well. It really works and the examples of machine learning applications in most world-known companies prove that fact. However, one thing is that your customization should be useful and really correspond with customers' needs.

For this purpose not only machine learning is generally used, but machine learning with Big Data. The reason is that Big Data collects the information, while machine learning processes it as well as learns from it. That is needed in order to make further predictions based on certain data.

The more tandem Big Data and machine learning collects and processes users' data, the more you know about what features they use often or do not use at all. This way, you extend your knowledge of the audience and you can adjust your app in accordance to their preferences to make your service even better.

Such a combination is already applied by Amazon and Google in some of their services.

Among all the possible machine learning applications methods, this technology has found it's place in medicine as well. It is not a secret to anybody that a modern iOS or android app development service allow you to implement person's heartbeat tracking, steps counting, etc. However, using machine learning applications, your app will be able to monitor users' activity constantly. It means that users should not change the settings in the app depending on what kind of activity. Whether it is jogging or cycling - the app understands that and makes all the necessary changes automatically.

It is easy to see the practicality of machine learning in business world. You will barely find a sphere where this technology could not be applied. Thus, in healthcare, it could be used to achieve a more detailed recognition of tumors at different stages of development.

Another example is smart home technologies. Here, machine learning can be used to monitor of apartments remotely. For example, there are already several machine learning applications in industry of smart homes. These solutions allow the house owner to learn when there is movement in a house with push-up notifications or messages.

Let the figures speak for themselves. One of the machine learning practical examples is one a big U.S. bank that has applied the technology for analyzing credit card transactions. Underneath, you may see what they got as a result.

Now, you at least have an idea of what machine learning is and how useful for businesses and the IT industry in general it is. So, it is high time to learn how to implement these magic algorithms. It is worth noting that there are already several ready-made machine learning tools intended to somehow simplify the work for your developers.

Google launched it's machine learning service called Awareness API last year. This service allows developers to understand the context in which customers use their smartphones. In total, there are 7 types of context - time, location, places, activity (jogging, cycling etc.), beacons, headphones, and temperature.

In turn, this API is divided into two components - Snapshot API and Fence API. These tools help in regulation and tracking of some already mentioned context parameters. This technology has already been adopted by the creators of such applications as Trulia and SuperPlayer Music.

Except for these machine learning tools, Google also provides developers with Text API intended for visual text recognition. The service supports the recognition of a majority of European languages in real-time mode. The API could also be used for sorting photos containing text and several more purposes.

Your developers could also apply REST API interfaces that use previously prepared models of machine learning allowing it to cut on some development hours. Companies like HP, Google, and Microsoft already provide machine learning as a service. However, all of them cost money but have a trial version. Generally, these libraries will help you to implement recognition of emotions, sex, age etc.

The last group of machine learning services are the basis for further machine learning model development. They include some predetermined features that could be expanded or complemented by your developers. To these platforms we can refer:

• Amazon Machine Learning Services that are able to notify you about suspicious transactions, select personalized content, predict user activity etc.
• BigML that helps to predict further sales, recognize handwritten symbols & speech etc.
• Microsoft Azure Machine Learning will help in natural language processing, pattern recognition, predictive modeling, and delivering the recommendations etc. 

All the above-mentioned are secure technologies, so there is no need to worry about users' data.

So, what's the difference between machine learning and AI? Machine learning has already become a reality. The usage of this technology contributes to better customer relationships making your app more diverse and useful.

The market is full of solutions and it may be hard to find out which of them fits your project most of all. In case you need a reliable machine learning developer, our team will be glad to help you. Contact us to get your free consultation.